Pytorch LinearModel

import torch


x_data = torch.Tensor([[1.0],[2.0],[3.0]])
y_data = torch.Tensor([[2.0],[4.0],[6.0]])


class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel,self).__init__()
        self.linear = torch.nn.Linear(1,1)

    def forward(self,x):
        y_pred = self.linear(x)
        return y_pred


model = LinearModel()
print(model)
params = list(model.parameters())
print(params)
criterion = torch.nn.MSELoss(size_average=False)
#optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)
#optimizer = torch.optim.Adam(model.parameters(),lr=0.01)
#optimizer = torch.optim.Adagrad(model.parameters(),lr= 0.01)
#optimizer = torch.optim.ASGD(model.parameters(), lr=0.01)
#optimizer = torch.optim.LBFGS(model.parameters(),lr = 0.01)
#optimizer = torch.optim.RMSprop(model.parameters(),lr = 0.01)
optimizer = torch.optim.Rprop(model.parameters(),lr = 0.01)
for epoch in range(100):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch, loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step(closure=float)
print('w = ',model.linear.weight.item())
print('b = ',model.linear.bias.item())

x_test = torch.Tensor([[4.0]])
y_test = model(x_test)
print('y_pred = ', y_test.data)